Quantitative paper-based SERS method for the rapid determination of sulfur amino acid residues in Pisum sativum
Bibliographic record
Abstract
It is known that peas, a sustainable ingredient in plant-based meat analogs and other proteinaceous food products, contain low levels of sulfur amino acid (SAA) residues. Developing additional inexpensive and rapid methods for determining SAA residues in protein is key to alleviating or resolving any concerns of possible micronutrient deficiencies associated with pea protein. This study evaluated surface-enhanced Raman spectroscopy (SERS) and the quantification of nascent signals stemming from cysteine residues in complex sample matrix solutions with low concentrations of analytes chemisorbed to silver using timed exposures. Silver nanoparticle printed SERS (Ag P-SERS) substrates showed a dynamic range of 1-9 ppm for cysteine and 0-42,000 ppm for bovine serum albumin (BSA). A distinct peak at 667 cm -1 in the SERS spectra of pea extracts corresponded to the ν(C-S) stretching mode of cysteine residues. The results demonstrate that low levels of Cys could be rapidly quantified with SERS and used to differentiate pea flour from 10 cultivars. This scientific development could have a far-reaching impact on the development of plant-based protein sources with nutritional profiles that rival those of animal proteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".